1. Field of the Subject Technology
The present subject technology relates to the use of advanced linear and nonlinear signal analysis for the detection of seizures and the interpretation of critical neurological conditions in the brain's electrical activity, such as the Electroencephalogram (EEG).
2. Background Art
Epilepsy is a condition characterized by recurrent seizures which are the outward manifestation of excessive or hypersynchronous abnormal electrical activity of neurons in the cerebral cortex of the brain. A seizure patient may suffer from several different types of seizures, or any combination thereof. For example, a common type of epilepsy is called the grand mal seizure, which is manifested by symptoms of convulsions with tonic-clonic contractions of muscles. Another type of epilepsy is called the absence seizure, which is characterized by brief and sudden loss of consciousness. Other types of seizures include complex partial seizure, which is characterized by a complete loss of consciousness, and psychomotor seizure, which is characterized by clouding of consciousness for one to two minutes. Some types of seizures may involve the entire brain, while other types of seizures may affect only a local portion of the brain.
EEGs have been employed to record electrical signals generated by different parts of the brain. In a typical EEG, a plurality of electrodes are placed across the scalp of a patient with predetermined spacing. FIGS. 3A and 3B show diagrams illustrating a typical arrangement of electrodes positioned on the scalp of an epilepsy patient along standard lines of measurements. The voltage waveform across a given pair of electrodes in the montage of an EEG recording is commonly referred to as a channel. A seizure is typically manifested by a highly rhythmic pattern of voltage waveforms on an EEG recording. However, depending upon the individual patient, different types of seizures, and various other factors, detection of an onset of seizure is sometimes not readily discernable by a human reader from a montage of an EEG recording. For example, sometimes a seizure may manifest itself as a random waveform pattern across a montage of an EEG recording. Sometimes recording errors may occur in one or more channels of a montage of an EEG recording. Sometimes an onset of seizure is not shown on an EEG recording as a rhythmic pattern of waveforms, but rather as an abnormal change from the background waveform pattern.
A system that is able to provide a real time quantitative seizure susceptibility index and seizure detection can be very useful clinically. A clinical implementation of such a system can be utilized immediately in a variety of hospital settings to improve patient safety, reduce staffing requirements, time doses of anticonvulsants, and to time ictal diagnostic procedures. Venues for the application include specialized emergency medical units (EMUs), intensive care units (ICUs), recovery rooms, and emergency treatment areas. However, in addition to inpatient monitoring applications, other monitoring applications such as use of the software in ambulatory recording devices may develop in the future.
A human reader of an EEG recording may need to go through hours or even days of recorded waveforms to determine the onset, duration, and type of seizures that may have occurred during that time. The human reader may miss an occurrence of a seizure, which is referred to as a false negative, or may mark a non-seizure segment of the waveforms as a seizure event, which is referred to as a false positive.
Conventional algorithms have been developed to assist a human reader in detecting seizures using traditional fast Fourier transform (FFT) or other spectral analysis techniques such as wavelet analysis. While these traditional techniques are usually effective in detecting highly rhythmic patterns of waveforms in order to identify a seizure event, some types of seizures which are not manifested by such highly rhythmic patterns may still be missed. Signal processing using conventional spectral analysis techniques may also sometimes return very high false positives, depending upon the parameters set by the algorithm.
Several U.S. patents and patent applications have pursued advancement of analysis. For example, U.S. Pat. No. 6,304,775 issued on Oct. 16, 2001, U.S. Pat. No. 7,263,467 issued Aug. 28, 2007, and U.S. Pat. No. 7,373,199 issued May 13, 2008, each of which is incorporated herein by reference, has advanced the technology. U.S. Patent Application Publication No. 2006/0287607 published on Dec. 21, 2006, which is incorporated herein by reference, has also advanced the technology.